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Develop your Large Language Models (LLM), RAG systems and Generative AI projects at your own desk, without being dependent on the cloud. GB10 Grace Blackwell chip and 128GB Unified Memory Remove the limits with .
With its KVKK compliant local development, low latency and huge memory capacity, it is the ideal solution for your corporate AI projects.
NVIDIA DGX Sparkis a revolutionary artificial intelligence computer packed into desktop (Small Form Factor - MFF) dimensions but aiming to deliver data center performance. At its heart, it combines the CPU and GPU in a single silicon GB10 Grace Blackwell Superchip bulunur.
Unlike traditional workstations, the biggest difference of DGX Spark is 128GB Unified Memory It is architecture. This way, you can run and train 70B+ parameter Large Language Models (Llama 3, Mixtral, etc.) locally on a single device, which can normally only be run on million dollar server clusters.
GB10 Grace Blackwell Superchip Architecture
Hardware details that offer desktop-sized supercomputer performance.
Huge pool of memory shared by the CPU and GPU. Load entire large models (70B+) into memory without VRAM bottleneck.
20 Core ARM CPU (10x Cortex-X + 10x Cortex-A) and Blackwell architecture GPU in one package. There is no bus delay.
Theoretical AI processing power of up to 1 Petaflops at FP4 precision. ~1000 Inference monster with TOPS (INT8).
Encrypted and high speed storage. Ample space for large data sets and model weights.
Fastest data transfer and network integration with 10 GbE Ethernet, Wi-Fi 7 and Bluetooth 5.3 .
Fully compatible with CUDA, cuDNN, TensorRT and NVIDIA AI Enterprise software stack. Container (NGC) support.
Llama 3 or Mistral models with your internal data (contracts, technical documents) fine-tune. Your data doesn't leave the office. Ideal memory capacity for LoRA/QLoRA techniques.
Retrieval Augmented Generation (RAG) Build architecture. Convert millions of documents into vector database (Milvus/FAISS) and perform semantic searches in seconds with LLM running on DGX Spark.
Scans your organization's private code base (Repo) and safely offers code suggestions to your developers, CodeLLaMA Run a native code assistant based on Local and secure alternative to GitHub Copilot.
Which hardware is suitable for your project? Here's the critical comparison:
| feature | NVIDIA DGX Spark | RTX 4090 Workstation | NVIDIA A100 (80GB) |
|---|---|---|---|
| Target Audience | AI Developers / R&D | Gaming / Entry Level AI | Data Center / Education |
| Memory (VRAM) | 128 GB Unified | 24 GB GDDR6X | 80 GB HBM2e |
| Large Model (70B+) Support | Full Support | Insufficient Memory | Support |
| Bus | On-Chip (Very Fast) | PCIe Bottleneck | NVLink (Fast) |
| Power Consumption | Low (ARM Efficiency) | High (450W+) | High (Data Center Cooling Required) |
| Place of Use | Desktop / Office | desktop | Rack Cabinet (Server Room) |
As Eka Sunucu, we recommend the following architecture for corporate use on DGX Spark:
Turkey (Turkish)
Germany (German)
Worldwide (English)
[Userlar] (Web / Teams / Slack)
|
v
[Security Katmanı] (SSO / LDAP / Keycloak)
|
v
[API Gateway] (Nginx / FastAPI) <--- (DGX Spark Üzerinde)
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+-------------------------------------------+
| |
v v
[LLM Engine] (vLLM / TensorRT-LLM) [RAG Orchestrator] (LangChain)
(Model: Llama 3 70B Quantized) |
| |
| v
| [Vektör DB] (Milvus / Qdrant)
| |
+-------------------------------------------+
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v
[Veri Kaynakları] (PDF, SQL, Docx - Yerel SSD)
You don't have to send your data to OpenAI or Cloud services.
Regulations in Turkey (KVKK, banking laws, etc.) generally restrict the export of sensitive data abroad. DGX Spark, tamamen On-Premise (Local Installation) Ensures that your data never leaves your office or server room by operating in place.
It is the safest AI development environment for law firms, healthcare institutions, financial companies, and defense industries.
If your project exceeds desktop dimensions, check out our data center solutions:
No. The DGX Spark has a Grace Blackwell architecture optimized for artificial intelligence workloads (FP4, INT8 calculations). It is not designed for game performance (DirectX/Vulkan). GeForce RTX series cards are recommended for games.
ARM64-based Linux distributions (especially Ubuntu) work. NVIDIA has optimized its AI Enterprise software stack on Ubuntu. Windows support may be limited or require virtualization.
Yes, we offer supply, customs clearance, and installation services with a special order (Pre-Order) method for your corporate projects. We also provide consulting services for software architecture that will work on the device.
Large Language Models (LLM) occupy memory space according to their parameter numbers. For example, a 70 billion parameter model requires at least 40-80 GB of VRAM to run. Standard graphics cards (24 GB) cannot meet this requirement, while the DGX Spark with 128 GB can handle these models with ease.
Consult with our expert team for DGX Spark or GPU Server Rental options.